diff --git a/src/ess/sans/beam_center_finder.py b/src/ess/sans/beam_center_finder.py
index ef9e82ef..4613be2e 100644
--- a/src/ess/sans/beam_center_finder.py
+++ b/src/ess/sans/beam_center_finder.py
@@ -37,6 +37,128 @@ def _xy_extrema(pos: sc.Variable) -> sc.Variable:
     return sc.concat([x_min, x_max, y_min, y_max], dim='extremes')
 
 
+def _find_beam_center(
+    data,
+    sample_holder_radius,
+    sample_holder_arm_width,
+):
+    '''
+    Each iteration the center of mass of the remaining intensity is computed
+    and assigned to be the current beam center guess ``c``.
+    Then three symmetrical masks are created to make sure that the remaining intensity
+    distribution does not extend outside of the detector and that the sample holder
+    does not make the remaining intensity asymmetrical.
+
+    The three masks are:
+
+      - one "outer" circular mask with radius less than the minimal distance
+        from the current beam center guess to the border of the detector
+      - one "inner" circular mask with radius larger than the sample holder
+      - one "arm" rectangular mask with width wider than the sample holder arm
+
+    The "outer" mask radius is found from the detector size.
+    The "inner" mask radius is supplied by the caller.
+    The "arm" mask slope is determined by the direction of minimum intensity
+    around the current beam center guess, the "arm" mask width is an argument
+    supplied by the caller.
+    '''
+    m = data.copy()
+    m.masks.clear()
+    s = m.bins.sum()
+
+    for i in range(20):
+        c = (s.coords['position'] * sc.values(s)).sum() / sc.values(s).sum()
+        d = s.coords['position'] - c.data
+
+        outer = 0.9 * min(
+            sc.abs(d.fields.x.min()),
+            sc.abs(d.fields.x.max()),
+            sc.abs(d.fields.y.min()),
+            sc.abs(d.fields.y.max()),
+        )
+        s.masks['_outer'] = d.fields.x**2 + d.fields.y**2 > outer**2
+        s.masks['_inner'] = d.fields.x**2 + d.fields.y**2 < sample_holder_radius**2
+
+        if i > 10:
+            s.coords['th'] = sc.where(
+                d.fields.x > sc.scalar(0.0, unit='m'),
+                sc.atan2(y=d.fields.y, x=d.fields.x),
+                sc.scalar(sc.constants.pi.value, unit='rad')
+                - sc.atan2(y=d.fields.y, x=-d.fields.x),
+            )
+            h = s.drop_masks(['_arm'] if '_arm' in s.masks else []).hist(th=100)
+            th = h.coords['th'][np.argmin(h.values)]
+
+            slope = sc.tan(th)
+            s.masks['_arm'] = (
+                d.fields.y < slope * d.fields.x + sample_holder_arm_width
+            ) & (d.fields.y > slope * d.fields.x - sample_holder_arm_width)
+    return c.data
+
+
+def beam_center_from_center_of_mass_alternative(
+    workflow,
+    sample_holder_radius=None,
+    sample_holder_arm_width=None,
+) -> BeamCenter:
+    """
+    Estimate the beam center via the center-of-mass of the data counts.
+
+    We are assuming the intensity distribution is symmetric around the beam center.
+    Even if the intensity distribution is symmetric around the beam center
+    the intensity distribution in the detector might not be, because
+
+        - the detector has a finite extent,
+        - and there is a sample holder covering part of the detector.
+
+    To deal with the limited size of the detector a mask can be applied that is small
+    enough so that the the remaining intensity is entirely inside the detector.
+    To deal with the sample holder we can mask the region of the detector that the
+    sample holder covers.
+
+    But to preserve the symmetry of the intensity around the beam center the masks
+    also need to be symmetical around the beam center.
+    The problem is, the beam center is unknown.
+    However, if the beam center was known to us, and we applied symmetrical masks
+    that covered the regions of the detector where the intensity distribution is
+    asymmetrical,
+    then the center of mass of the remaining intensity would equal the beam center.
+    Conversely, if we apply symmetrical masks around a point that is not the beam center
+    the center of mass of the remaining intensity will (likely) not equal the original
+    point.
+    This suggests the beam center can be found using a fixed point iteration where each
+    iteration we
+
+    1. Compute the center of mass of the remaining intensity and assign it to be our
+       current estimate of the beam center.
+    2. Create symmetrical masks around the current estimate of the beam center.
+    3. Repeat from 1. until convergence.
+
+    Parameters
+    ----------
+    workflow:
+        The reduction workflow to compute MaskedData[SampleRun].
+
+    Returns
+    -------
+    :
+        The beam center position as a vector.
+    """
+
+    if sample_holder_radius is None:
+        sample_holder_radius = sc.scalar(0.05, unit='m')
+    if sample_holder_arm_width is None:
+        sample_holder_arm_width = sc.scalar(0.02, unit='m')
+
+    try:
+        beam_center = workflow.compute(BeamCenter)
+    except sciline.UnsatisfiedRequirement:
+        beam_center = sc.vector([0.0, 0.0, 0.0], unit='m')
+        workflow[BeamCenter] = beam_center
+    data = workflow.compute(MaskedData[SampleRun])
+    return _find_beam_center(data, sample_holder_radius, sample_holder_arm_width)
+
+
 def beam_center_from_center_of_mass(workflow: sciline.Pipeline) -> BeamCenter:
     """
     Estimate the beam center via the center-of-mass of the data counts.